@inproceedings{conf/percom/ChoMLKG10,
  added-at = {2012-02-21T00:00:00.000+0100},
  author = {Cho, Dae-Ki and Mun, Min and Lee, Uichin and Kaiser, William J. and Gerla, Mario},
  biburl = {http://www.bibsonomy.org/bibtex/21a914cee3c8801b7ae370d8d65036368/dblp},
  booktitle = {PerCom},
  crossref = {conf/percom/2010},
  ee = {http://dx.doi.org/10.1109/PERCOM.2010.5466984},
  interhash = {91339d818c0312324e8fdb3043f7ad96},
  intrahash = {1a914cee3c8801b7ae370d8d65036368},
  keywords = {dblp},
  pages = {116-124},
  publisher = {IEEE Computer Society},
  timestamp = {2012-02-21T00:00:00.000+0100},
  title = {AutoGait: A mobile platform that accurately estimates the distance walked.},
  url = {http://dblp.uni-trier.de/db/conf/percom/percom2010.html#ChoMLKG10},
  year = 2010
}
  	
@article{0957-0233-20-1-015203,
  author={Zuolei Sun and Xuchu Mao and Weifeng Tian and Xiangfen Zhang},
  title={Activity classification and dead reckoning for pedestrian navigation with wearable sensors},
  journal={Measurement Science and Technology},
  volume={20},
  number={1},
  pages={015203},
  url={http://stacks.iop.org/0957-0233/20/i=1/a=015203},
  year={2009},
  abstract={This paper addresses an approach which integrates activity classification and dead reckoning techniques in step-based pedestrian navigation. In the proposed method, the pedestrian is equipped with a prototype wearable sensor module to record accelerations and determine the headings while walking. To improve the step detection accuracy, different types of activities are classified according to extracted features by means of a probabilistic neural network (PNN). The vertical acceleration data, which indicate the periodic vibration during gait cycle are filtered through a wavelet transform before being used to count the steps and assess the step length from which the distance traveled is estimated. By coupling the distance with the azimuth, navigation through pedestrian dead reckoning is implemented. This research provides a possible seamless pedestrian navigation solution which can be applied to a wide range of areas where the global navigation satellite system (GNSS) signal remains vulnerable. Results of two experiments in this paper reveal that the proposed approach is effective in reducing navigation errors and improving accuracy.}
}
	
@article{inbuildingLocalization,
  added-at = {2011-03-07T14:34:57.000+0100},
  author = {Parnandi, Avinash and Le, Ken and Vaghela, Pradeep and Kolli, Aalaya and Dantu, Karthik and Poduri, Sameera and Sukhatme, Gaurav S.},
  biburl = {http://www.bibsonomy.org/bibtex/2f8eaa17cf3c260b19216977ff80d78f7/michaelfessler},
  interhash = {7196a7bd4af9dfdcce15c761bef4e439},
  intrahash = {f8eaa17cf3c260b19216977ff80d78f7},
  keywords = {accelerometers building coarse localization smartphones},
  timestamp = {2011-03-07T14:34:57.000+0100},
  title = {Coarse In-Building Localization with Smartphones},
  year = {2009?}
}

@ARTICLE{1542534, 
author={Lei Fang and Antsaklis, P.J. and Montestruque, L.A. and McMickell, M.B. and Lemmon, M. and Yashan Sun and Hui Fang and Koutroulis, I. and Haenggi, M. and Min Xie and Xiaojuan Xie}, 
journal={Instrumentation and Measurement, IEEE Transactions on}, title={Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience}, 
year={2005}, 
month={dec.}, 
volume={54}, 
number={6}, 
pages={ 2342 - 2358}, 
abstract={ In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.}, 
keywords={ NavMote; accelerometers; dynamic calibrations; heading errors; inertial sensing; integrated magnetic compass; map matching; pedestrian navigation system; static calibrations; step detection; step length estimation; wireless assisted pedestrian dead reckoning system; wireless sensor network; wireless telemetry; inertial navigation; mobile radio; radiotelemetry; wireless sensor networks;}, 
doi={10.1109/TIM.2005.858557}, 
ISSN={0018-9456},}

@inproceedings{Iso:2006:GAB:1152215.1152244,
 author = {Iso, Toshiki and Yamazaki, Kenichi},
 title = {Gait analyzer based on a cell phone with a single three-axis accelerometer},
 booktitle = {Proceedings of the 8th conference on Human-computer interaction with mobile devices and services},
 series = {MobileHCI '06},
 year = {2006},
 isbn = {1-59593-390-5},
 location = {Helsinki, Finland},
 pages = {141--144},
 numpages = {4},
 url = {http://doi.acm.org/10.1145/1152215.1152244},
 doi = {10.1145/1152215.1152244},
 acmid = {1152244},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {accelerometer, cell phone, context, gait analysis, self-organizing map, sensor, ubiquitous service, wavelet packet},
} 

@INPROCEEDINGS{5507241, 
author={Shin, S. H. and Lee, M. S. and Park, C. G. and Hong, Hyun Su}, 
booktitle={Position Location and Navigation Symposium (PLANS), 2010 IEEE/ION}, title={Pedestrian dead reckoning system with phone location awareness algorithm}, 
year={2010}, 
month={may}, 
pages={97 -101}, 
abstract={In this paper we present a PDR (Pedestrian Dead Reckoning) system with a phone location awareness algorithm. PDR is a device which provides position information of the pedestrian. In general, the step length is estimated using a linear combination of the walking frequency and the acceleration variance for the mobile phone. It means that the step length estimation accuracy is affected by coefficients of the walking frequency and the acceleration variance which are called step length estimation parameters. Developed PDR is assumed that it is embedded in the mobile phone. Thus, parameters can be different from each phone location such as hand with swing motion, hand without any motion and pants pocket. It means that different parameters can degrade the accuracy of the step length estimation. Step length estimation result can be improved when appropriate parameters which are determined by phone location awareness algorithm are used. In this paper, the phone location awareness algorithm for PDR is proposed.}, 
doi={10.1109/PLANS.2010.5507241}, 
ISSN={2153-358X},}

@INPROCEEDINGS{5955295, 
author={Henpraserttae, A. and Thiemjarus, S. and Marukatat, S.}, 
booktitle={Body Sensor Networks (BSN), 2011 International Conference on}, title={Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location}, 
year={2011}, 
month={may}, 
pages={41 -46}, 
abstract={This paper investigates two major issues in using a tri-axial accelerometer-embedded mobile phone for continuous activity monitoring, i.e. the difference in orientations and locations of the device. Two experiments with a total of ten test subjects performed six daily activities were conducted in this study: one with a device fixed on the waist in sixteen different orientations and another with three different device locations (i.e., shirt-pocket, trouser-pocket and waist) in two different device orientations. For handling with varying device orientations, a projection-based method for device coordinate system estimation has been proposed. Based on the dataset with sixteen different device orientations, the experimental results have illustrated that the proposed method is efficient for rectifying the acceleration signals into the same coordinate system, yielding significantly improved activity recognition accuracy. After signal transformation, the recognition results of signals acquired from different device locations are compared. The experimental results show that when the sensor is placed on different rigid body, different models are required for certain activities.}, 
keywords={acceleration signal;activity recognition;device coordinate system estimation;mobile device location;mobile device orientation;signal transformation;tri-axial accelerometer-embedded mobile phone;accelerometers;mobile handsets;pattern recognition;signal processing;}, 
doi={10.1109/BSN.2011.8}, 
}

@INPROCEEDINGS{6038786, 
author={Chan, H.K.Y. and Huiru Zheng and Haiying Wang and Gawley, R. and Mingjing Yang and Sterritt, R.}, 
booktitle={Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2011 5th International Conference on}, title={Feasibility study on iPhone accelerometer for gait detection}, 
year={2011}, 
month={may}, 
pages={184 -187}, 
abstract={Falls amongst the elderly is becoming a major problem with over 50% of elderly hospitalizations due to injury from fall related accidents. Healthcare expenses are dramatically rising due to growing elderly population. Many current technologies for gait analysis are laboratory-based and can incur substantial costs for the healthcare sector for treatment of falls. However utilization of alternative commercially available technologies can potentially reduce costs. Accelerometers are one such option, being ambulatory motion sensors for the detection of orientation and movement. Smart mobile devices are considered as non-invasive and increasingly contain accelerometers for detecting device orientation. This study looks at the capabilities of the accelerometer within a smart mobile device, namely the iPhone, for identification of gait events from walking along a flat surface. The results prove that it is possible to extract features from the accelerometer of an iPhone such as step detection, stride time and cadence.}, 
keywords={ambulatory motion sensors;elderly hospitalizations;elderly population;fall related accidents;gait analysis;gait detection;healthcare;healthcare sector;iPhone accelerometer;smart mobile device;accelerometers;gait analysis;health care;mobile computing;mobile handsets;}, 
}

@INPROCEEDINGS{974027, 
author={Seon-Woo Lee and Mase, K.}, 
booktitle={Control Applications, 2001. (CCA '01). Proceedings of the 2001 IEEE International Conference on}, title={Recognition of walking behaviors for pedestrian navigation}, 
year={2001}, 
pages={1152 -1155}, 
abstract={This paper presents a method for detecting and classifying walking behaviors based on acceleration measurements of a pedestrian, and is employed in an indoor navigation system currently being developed. The prototype navigation system uses a set of inexpensive and wearable sensors: a bi-axial accelerometer, a digital compass, and an infrared light detector. Using the measured acceleration data, the proposed method can detect forward steps and classify the steps as: "level ground", "up", and "down". The objective of the detection is to count steps for estimating the current position by dead-reckoning using heading measurements. The capability in detecting "up/down" steps can be used to correct estimated position errors. The effectiveness of the proposed method is demonstrated by experiments on six persons}, 
keywords={IR detector;acceleration measurements;bi-axial accelerometer;dead-reckoning;digital compass;estimated position errors;heading measurements;indoor navigation system;inexpensive wearable sensors;infrared light detector;pedestrian navigation;walking behavior classification;walking behavior detection;walking behavior recognition;computerised navigation;gait analysis;pattern recognition;}, 
doi={10.1109/CCA.2001.974027}, 
}

@book{margaria1976biomechanics,
  title={Biomechanics and energetics of muscular exercise},
  author={Margaria, R.},
  isbn={9780198573975},
  lccn={77352451},
  url={http://books.google.co.uk/books?id=GEhrAAAAMAAJ},
  year={1976},
  publisher={Clarendon Press}
}
@INPROCEEDINGS{Bao04activityrecognition,
    author = {Ling Bao and Stephen S. Intille},
    title = {Activity recognition from user-annotated acceleration data},
    booktitle = {},
    year = {2004},
    pages = {1--17},
    publisher = {Springer}
}

@INPROCEEDINGS{Ravi05activityrecognition,
    author = {Nishkam Ravi and Nikhil D and Preetham Mysore and Michael L. Littman},
    title = {Activity recognition from accelerometer data},
    booktitle = {In Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence(IAAI},
    year = {2005},
    pages = {1541--1546},
    publisher = {AAAI Press}
}
@INPROCEEDINGS{5334925, 
author={Xi Long and Bin Yin and Aarts, R.M.}, 
booktitle={Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE}, title={Single-accelerometer-based daily physical activity classification}, 
year={2009}, 
month={sept.}, 
pages={6107 -6110}, 
abstract={In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of ~80%, which was comparable with that obtained by a Decision Tree classifier.}, 
keywords={Bayesian classification;cycling;daily energy expenditure;daily physical activity classification;decision tree based approach;driving;feature vector dimension;principal components analysis;running;single triaxial accelerometer;sports;waist;walking;accelerometers;biomechanics;biomedical equipment;biomedical measurement;medical signal processing;principal component analysis;signal classification;Acceleration;Actigraphy;Activities of Daily Living;Algorithms;Equipment Design;Equipment Failure Analysis;Humans;Monitoring, Ambulatory;Motor Activity;Pattern Recognition, Automated;Reproducibility of Results;Sensitivity and Specificity;Transducers;}, 
doi={10.1109/IEMBS.2009.5334925}, 
ISSN={1557-170X},}

@inproceedings{Sprager:2009:GIU:1736242.1736261,
 author = {Sprager, Sebastijan and Zazula, Damjan},
 title = {Gait identification using cumulants of accelerometer data},
 booktitle = {Proceedings of the 2nd WSEAS International Conference on Sensors, and Signals and Visualization, Imaging and Simulation and Materials Science},
 series = {SENSIG'09/VIS'09/MATERIALS'09},
 year = {2009},
 isbn = {978-960-474-135-9},
 location = {Baltimore, USA},
 pages = {94--99},
 numpages = {6},
 url = {http://dl.acm.org/citation.cfm?id=1736242.1736261},
 acmid = {1736261},
 publisher = {World Scientific and Engineering Academy and Society (WSEAS)},
 address = {Stevens Point, Wisconsin, USA},
 keywords = {accelerometer, body sensor, cumulants, gait identification, gait recognition, high-order statistics, pattern recognition},
}
@inproceedings{Plasqui:1994,
 author = {Guy Plasqui, Annemiek M.C.P. Joosen, Arnold D. Kester, Annelies H.C. Goris and Klaas R. Westerter},
 title = {Measuring Free-Living Energy Expenditure and Physical Activity with Triaxial Accelerometry},
 booktitle = {Obesity Research},
 year = {1994},
 pages = {1363--1369},
 numpages = {6},
 publisher = {Obesity Research},
 keywords = {accelerometer, body sensor, cumulants, gait identification, gait recognition, high-order statistics, pattern recognition},
}

@article{maguire2009comparison,
  title={Comparison of feature classification algorithm for activity recognition based on accelerometer and heart rate data},
  author={Maguire, D. and Frisby, R.},
  journal={9th. IT \& T Conference},
  pages={11},
  year={2009}
}
 % WEB PAGES
@MISC{weka:wiki,
      AUTHOR = "Wikipedia",
      TITLE = "Weka Page",
      YEAR = {2012},
      NOTE = "\url{http://en.wikipedia.org/wiki/Weka\_(machine\_learning)}",
}

@MISC{android:sensoreventswhilescreenoff,
      AUTHOR = "jameson@nosemaj.org",
      TITLE = "Getting Android Sensor Events While The Screen is Off",
      YEAR = {2012},
      NOTE = "\url{http://nosemaj.org/android-persistent-sensors}",
}
@MISC{android:apiguide,
      AUTHOR = "Google",
      TITLE = "The official Android developer guides and references",
      YEAR = {2012},
      NOTE = "\url{http://developer.android.com/guide/components/index.html}",
}
@MISC{android:dominance,
      AUTHOR = "Signals and Systems Telecom",
      TITLE = "Android Smartphone Activations Reached 331 Million in Q1'2012",
      YEAR = {2012},
      NOTE = "\url{http://www.prweb.com/releases/2012/5/prweb9514037.htm}",
}
@MISC{android:versiondistribution,
      AUTHOR = "Google",
      TITLE = "Platform versions - number of active devices running a given version of the Android platform",
      YEAR = {2012},
      NOTE = "\url{http://developer.android.com/about/dashboards/index.html}",
}
@MISC{jfreechart:website,
      AUTHOR = "Under the terms of LGPL",
      TITLE = "An open-source framework for Java, which allows the creation of a wide variety of charts",
      YEAR = {2012},
      NOTE = "\url{http://www.jfree.org/jfreechart/}",
}
@MISC{apache:mathslib,
      AUTHOR = "The Apache Software Foundation",
      TITLE = "Commons Math is a library of lightweight, self-contained mathematics and statistics components addressing the most common problems not available in the Java programming",
      YEAR = {2012},
      NOTE = "\url{http://commons.apache.org/math/}",
}
@MISC{signal:smoothing,
      AUTHOR = "Prof. Tom O'Haver , Department of Chemistry and Biochemistry, The University of Maryland at College Park",
      TITLE = "Smoothing techniques to reduce noise in a signal",
      YEAR = {2012},
      NOTE = "\url{http://terpconnect.umd.edu/~toh/spectrum/Smoothing.html}",
}

